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Keynote Lectures

Data-Driven Requirements Engineering: The Way Ahead
Xavier Franch, Universitat Politècnica de Catalunya, Spain

Subjective Databases
Alon Halevy, Facebook AI, United States

 

Data-Driven Requirements Engineering: The Way Ahead

Xavier Franch
Universitat Politècnica de Catalunya
Spain
 

Brief Bio
Xavier Franch is professor at the Universitat Politècnica de Catalunya (UPC-BarcelonaTech), where he leads the GESSI research group (https://gessi.upc.edu/en). Active researcher with more than 200 peer-reviewed publications, his research interests include Requirements Engineering, System Modeling, Software Evolution and Adaptation, and Agile Software Development, among others. In the EU framework programmes, he coordinated the Q-Rapids (H2020, 2016-2019) and RISCOSS (FP7, 2012-2015) projects, acted as scientific manager in SUPERSEDE (H2020, 2015-2018) and participated in OpenReq (H2020, 2017-2019). He is editorial board member of the following journals: IST (Elsevier), REJ and Computing (Springer), and IJCIS (World Scientific); as well as Deputy Editor of IET Software and Journal First chair in JSS (Elsevier). He belongs to the steering committee of several major conferences (remarkably IEEE RE and CAiSE) and has occupied several conference positions in major software engineering conferences conferences, remarkably as general chair (PROFES'9 and RE’08) and program chair (RE’16, ICSOC’14, CAiSE’12 and REFSQ'11). He is full member of the International Requirements Engineering Board (IREB) association (also as part of the organization’s Council). He has won several best papers awards. He has taught tutorials and organized workshops on software engineering-related topics in several major conferences as ICSE, RE, CAiSE. More details at https://www.essi.upc.edu/~franch/.


Abstract
Data-driven requirements engineering is becoming increasingly widespread in the development of today's software systems, services and apps. The exploitation of data coming from the user through several sources may indeed become an extremely useful input to requirements elicitation and management, but it does not come for free. Techniques such as NLP and ML are difficult to master and require high-quality data, whilst their generalization remains a challenge. Also, understanding the consequences into the companies' development practices is still an open issue. In this keynote, I summarize the main concepts behind data-driven requirements engineering, then I provide an overview of the state of the art, recapitulate lessons learned and open challenges, and outline future research areas especially related to the impact of this approach into the full software development process.



 

 

Subjective Databases

Alon Halevy
Facebook AI
United States
 

Brief Bio
Alon Halevy joined Facebook AI in August, 2019. Until December, 2018, Alon was the CEO of Megagon Labs where his team focused on developing AI for well-being. Before that, Alon led the Structured Data Research Group at Google for 10 years. Previously,  he was a professor of computer science at the University of Washington, where he founded the database research group. Alon is a founder Nimble Technology, and of Transformatic, Inc., which was acquired by Google in 2005. He is the author of "The Infinite Emotions of Coffee" and co-author of "Principles of Data Integration". Alon is an ACM Fellow, received the Sloan Fellowship and the Presidential Early Career Awards for Scientists and Engineers (PECASE) Award. He received his Ph.D. in Computer Science from Stanford University in 1993 and his bachelor’s degree from the Hebrew University of Jerusalem.


Abstract
Online consumers are constantly seeking experiences, such as vacations, restaurant outings and exciting jobs in order to improve their well-being. However, e-commerce search engines only support searches for experiences to a very limited extent -- you can search on the objective attributes of a service (e.g., hotel price and location), but the experiential aspects are buried in online reviews. E-commerce sites make some effort to surface comments from reviews, but users can still not specify experiential aspects (e.g., romantic hotel in a quiet Mediterranean town) in their queries. There has been considerable work in the NLP community to recognize and extract subjective text, but that’s only the first step towards querying.
To address this challenge, we introduce OpineDB, a subjective database. OpineDB is based on a data model that carefully balances the richness and bottom-up nature of natural language and the top-down design principles of databases. OpineDB is able to answer queries that combine multiple subjective conditions and aggregate subjective data. Unlike a traditional database system, there may not be a 1-1 mapping between query terms and the database schema. In some cases, OpineDB needs to find the closest attribute (or combination of attributes) that answers a user query, and in some cases it may have to fall back to retrieval directly from the review text.
Joint work with Yuliang Li, Jinfeng Li, Vivian Li, Aaron Feng, Saran Mumick and Wang-Chiew Tan from Megagon Labs.



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